[Lisa_seminaires] [Tea Talk] Clement Farabet (NVIDIA) Mon Oct 9, 2pm, Claire-McNicoll Z-209

Michael Noukhovitch mnoukhov at gmail.com
Ven 6 Oct 13:01:36 EDT 2017


*Update*: NVIDIA said they're raffling off a Titan Xp!

*Also note:* I realize that this Monday is also thanksgiving and due to
scheduling, I can't change the tea talk. I'm sorry to anyone whose weekend
plans are being interrupted, that's my bad. I'll make sure not to schedule
any other tea talks on holidays.

Finally a *reminder:* if you do plan to attend, please sign up on the
eventbrite link:
https://www.eventbrite.com/e/mila-tea-talk-tickets-38158869205

Thanks,
Michael

On Tue, Oct 3, 2017 at 2:01 PM Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

>
> Next week, we have a flurry of tea talks and it all starts with VP of AI
> infrastructure at NVIDIA, *Clement Farabet* on *Monday October 9th *at
> *2pm* at *Pavilion Claire McNicoll room Z-209*.
>
> There will be a *small social *after the event so you can talk directly
> with NVIDIA researchers! NVIDIA has also been incredibly kind and will
> bring *FREE FOOD* as well as hold a *RAFFLE!* I don't know what the
> raffle is for, but will leave it your imagination ;)
>
> This is an awesome event, and hope everyone can make it! If you plan to
> attend, please sign up on their eventbrite link:
> https://www.eventbrite.com/e/mila-tea-talk-tickets-38158869205
>
> *KEYWORDS *Infrastructure, Self-driving Cars, Domain Transfer
>
> *TITLE*
>
> Deep Learning Research at NVIDIA
>
>
>
> *ABSTRACT*
>
> NVIDIA is the leading platform for Deep Learning research and invests in
> a broad range of research projects within key industries, such as graphics
> and self-driving cars. In this talk I will discuss a few of our projects
> in-depth: from leveraging synthetic data to reduce our need on data;
> ray-tracing for real-time virtual worlds; auto face animation for game
> designers; and auto image transforms for photo editing. I will also talk
> about how we apply this research work into a product and operate a
> fast-moving R&D team to build autonomous vehicles.
>
>
> *BIO*
>
> Clement Farabet is VP of AI Infrastructure at NVIDIA. His team is
> responsible for building NVIDIA's next-generation AI platform, leveraging
> NVIDIA's hardware to enable a broad range of new applications, ranging
> from self-driving cars to medical imaging. Clement Farabet received a
> Master’s Degree in Electrical Engineering with honors from Institut
> National des Sciences Appliquées (INSA) de Lyon, France in 2008. His
> Master’s thesis work on reconfigurable hardware for deep neural networks
> was developed at the Courant Institute of Mathematical Sciences of New York
> University with Professor Yann LeCun, and led to a patent. He then joined
> Professor Yann LeCun’s laboratory in 2008, as a research scientist. In
> 2009, he started collaborating with Yale University’s e-Lab, led by
> Professor Eugenio Culurciello. This joint work later led to the creation of
> TeraDeep (www.teradeep.com). In 2010, he started the PhD program at
> Université Paris-Est, co-advised by Professors Laurent Najman and Yann
> LeCun. His thesis focused on real-time image understanding/parsing with
> deep convolutional networks. The main contributions of his thesis were
> multi-scale convolutional networks and graph-based techniques for efficient
> segmentations of class prediction maps. He graduated in 2013, and went on
> to cofound Madbits, a company that focused on representing, understanding
> and connecting images. Madbits was acquired by Twitter in 2014. At Twitter,
> he cofounded Cortex, a team of software engineers, data scientists, and
> research scientists dedicated to developing state-of-the-art machine
> learning capabilities to refine and enable new products. He subsequently
> lead and managed a team called Cortex Core, which focused on building a
> high-leverage modular machine/deep learning platform to power every aspect
> of the Twitter product (recommendation systems, search, timeline ranking,
> etc.). This team focused on (1) developing models of text, images, video,
> users, and (2) making these models seamlessly importable as components of
> user-facing ML systems.
>
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